Attributed Graph Clustering with Double Contrastive Projector

Attributed graph clustering is a fundamental yet challenging task for graph analysis, which is of significance in the real world. Recently, graph contrastive learning have demonstrated excellent performance on clustering task. However, we observe that existing methods have the following three defects: a) an end-to-end graph clustering framework based on contrastive clustering has not yet been implemented, b) they require a pretext task to obtain initial labels, c) and most methods lack a specific loss function for clustering processes. To address above issues, we innovatively propose Attributed Graph Contrastive Clustering (AGCC), which is a novel self-supervised attributed graph clustering framework. AGCC can directly produce a clustering assignment matrix without calculating target distribution matrix. Specifically, in our method, an effective module based on contrastive graph representation learning is being leveraged to extract node embeddings by node features and topological structure. In the phase of data augmentation, the module can adaptively mask node features and drops edges. Meanwhile, another reliable contrastive clustering module generates clustering results through contrasting the representation of each cluster. Comprehensive empirical analysis, compared with various types of classic and deep methods, demonstrates the efficacy and the scalability of the proposed method.